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Simulation has recently become key for deep reinforcement learning to safely and efficiently acquire general and complex control policies from visual and proprioceptive inputs.
Neuronlike adaptive elements that can solve difficult learning control problems
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U-net: Convolutional networks for biomedical image segmentation
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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al · 2015
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D.-A. Clevert, T. Unterthiner, and S. Hochreiter · 2015
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
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The beta policy for continuous control reinforcement learning
P.-W. Chou · 2017
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The TacTip Family: Soft Optical Tactile Sensors with 3D-Printed Biomimetic Morphologies
B. Ward-Cherrier, N. Pestell, L. Cramphorn, B. Winstone, M. E. Giannaccini, J. Rossiter, and N. F. Lepora · 2018
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Soft actor-critic algorithms and applications
T. Haarnoja, A. Zhou, K. Hartikainen, G. Tucker, S. Ha, J. Tan, V. Kumar, H. Zhu, A. Gupta, P. Abbeel, et al · 2018
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High-resolution image synthesis and semantic manipulation with conditional gans
T. Wang, M. Liu, J. Zhu, A. Tao, J. Kautz, and B. Catanzaro · 2018
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Sim-to-real reinforcement learning for deformable object manipulation
J. Matas, S. James, and A. J. Davison · 2018
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Solving rubik’s cube with a robot hand
I. Akkaya, M. Andrychowicz, M. Chociej, M. Litwin, B. McGrew, A. Petron, A. Paino, M. Plappert, G. Powell, R. Ribas, et al · 2019
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Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks
S. James, P. Wohlhart, M. Kalakrishnan, D. Kalashnikov, A. Irpan, J. Ibarz, S. Levine, R. Hadsell, and K. Bousmalis · 2019
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Design, Motivation and Evaluation of a Full-Resolution Optical Tactile Sensor
C. Sferrazza and R. D’Andrea · 2019
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From pixels to percepts: Highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor
N. F. Lepora, A. Church, C. De Kerckhove, R. Hadsell, and J. Lloyd · 2019
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Stable baselines3
A. Raffin, A. Hill, M. Ernestus, A. Gleave, A. Kanervisto, and N. Dormann · 2019
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Value constrained model-free continuous control
S. Bohez, A. Abdolmaleki, M. Neunert, J. Buchli, N. Heess, and R. Hadsell · 2019
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SoundSpaces: Audio-Visual Navigation in 3D Environments
C. Chen, U. Jain, C. Schissler, S. V. A. Gari, Z. Al-Halah, V. K. Ithapu, P. Robinson, and K. Grauman · 2019
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RLBench: The Robot Learning Benchmark & Learning Environment
S. James, Z. Ma, D. R. Arrojo, and A. J. Davison · 2019
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Rl-cyclegan: Reinforcement learning aware simulation-to-real
K. Rao, C. Harris, A. Irpan, S. Levine, J. Ibarz, and M. Khansari · 2020
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Optimal deep learning for robot touch: Training accurate pose models of 3d surfaces and edges
N. F. Lepora and J. Lloyd · 2020
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robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
Y. Zhu, J. Wong, A. Mandlekar, and R. Martín-Martín · 2020
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Difftaichi: Differentiable programming for physical simulation
Y. Hu, L. Anderson, T. Li, Q. Sun, N. Carr, J. Ragan-Kelley, and F. Durand · 2020
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NeuralSim: Augmenting Differentiable Simulators with Neural Networks
E. Heiden, D. Millard, E. Coumans, Y. Sheng, and G. S. Sukhatme · 2020
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Pybullet, a python module for physics simulation for games, robotics and machine learning
E. Coumans and Y. Bai · 2019
Cited alongside, same era.
Learning quadrupedal locomotion over challenging terrain
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter · 2020
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Learning the sense of touch in simulation: a sim-to-real strategy for vision-based tactile sensing
C. Sferrazza, T. Bi, and R. D’Andrea · 2020
Cited alongside, same era.
C. Sferrazza and R. D’Andrea · 2020
Cited alongside, same era.
Sim-to-Real Transfer for Optical Tactile Sensing
Z. Ding, N. F. Lepora, and E. Johns · 2020
Cited alongside, same era.
Tacto: A fast, flexible and open-source simulator for high-resolution vision-based tactile sensors
S. Wang, M. Lambeta, L. Chou, and R. Calandra · 2020
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Omnitact: A multi-directional high-resolution touch sensor
A. Padmanabha, F. Ebert, S. Tian, R. Calandra, C. Finn, and S. Levine · 2020
Cited alongside, same era.
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
I. Kostrikov, D. Yarats, and R. Fergus · 2020
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Reinforcement learning with augmented data
M. Laskin, K. Lee, A. Stooke, L. Pinto, P. Abbeel, and A. Srinivas · 2020
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How to train your robot with deep reinforcement learning: lessons we have learned
J. Ibarz, J. Tan, C. Finn, M. Kalakrishnan, P. Pastor, and S. Levine · 2021
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Zero-shot sim-to-real transfer of tactile control policies for aggressive swing-up manipulation
T. Bi, C. Sferrazza, and R. D’Andrea · 2021
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Soft biomimetic optical tactile sensing with the tactip: A review
N. F. Lepora · 2021
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Generation of gelsight tactile images for sim2real learning
D. F. Gomes, P. Paoletti, and S. Luo · 2021
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Opensimplex noise
S. Kurt and S. A · 2021
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Pose-based tactile servoing: Controlled soft touch using deep learning
N. F. Lepora and J. Lloyd · 2021
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Goal-driven robotic pushing using tactile and proprioceptive feedback
J. Lloyd and N. F. Lepora · 2021
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